{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "({0: 'O',\n  1: 'B-地块编码',\n  2: 'I-地块编码',\n  3: 'B-地块位置',\n  4: 'I-地块位置',\n  5: 'B-出让面积',\n  6: 'I-出让面积',\n  7: 'B-土地用途',\n  8: 'I-土地用途',\n  9: 'B-容积率',\n  10: 'I-容积率',\n  11: 'B-起始价',\n  12: 'I-起始价',\n  13: 'B-成交价',\n  14: 'I-成交价',\n  15: 'B-溢价率',\n  16: 'I-溢价率',\n  17: 'B-成交时间',\n  18: 'I-成交时间',\n  19: 'B-受让人',\n  20: 'I-受让人',\n  21: 'B-城市',\n  22: 'I-城市',\n  23: 'B-触发词',\n  24: 'I-触发词'},\n {'O': 0,\n  'B-地块编码': 1,\n  'I-地块编码': 2,\n  'B-地块位置': 3,\n  'I-地块位置': 4,\n  'B-出让面积': 5,\n  'I-出让面积': 6,\n  'B-土地用途': 7,\n  'I-土地用途': 8,\n  'B-容积率': 9,\n  'I-容积率': 10,\n  'B-起始价': 11,\n  'I-起始价': 12,\n  'B-成交价': 13,\n  'I-成交价': 14,\n  'B-溢价率': 15,\n  'I-溢价率': 16,\n  'B-成交时间': 17,\n  'I-成交时间': 18,\n  'B-受让人': 19,\n  'I-受让人': 20,\n  'B-城市': 21,\n  'I-城市': 22,\n  'B-触发词': 23,\n  'I-触发词': 24})"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trigger_label = '触发词'\n",
    "schemas = ['O', 'B-地块编码','I-地块编码', 'B-地块位置', 'I-地块位置', 'B-出让面积', 'I-出让面积', 'B-土地用途', 'I-土地用途', 'B-容积率', 'I-容积率',\n",
    "           'B-起始价', 'I-起始价', 'B-成交价', 'I-成交价', 'B-溢价率', 'I-溢价率', 'B-成交时间', 'I-成交时间', 'B-受让人', 'I-受让人',\n",
    "           'B-城市', 'I-城市', 'B-触发词', 'I-触发词']\n",
    "id2tag = {idx: tag for idx, tag in enumerate(schemas)}\n",
    "tag2id = {tag: idx for idx, tag in enumerate(schemas)}\n",
    "\n",
    "id2tag,tag2id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "from datasets import ClassLabel\n",
    "from transformers import BertForTokenClassification, AutoTokenizer\n",
    "\n",
    "import torch\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "modelpre = BertForTokenClassification.from_pretrained(\"xyfigo/MyLandNER\")\n",
    "tokenizerpre = AutoTokenizer.from_pretrained(\"xyfigo/MyLandNER\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "tags = ClassLabel(num_classes=len(schemas), names=schemas)\n",
    "\n",
    "def tag_text(text, tags, model, tokenizer):\n",
    "    # Get tokens with special characters\n",
    "    tokens = tokenizer(text).tokens()\n",
    "    # Encode the sequence into IDs\n",
    "    input_ids = tokenizerpre(text, return_tensors=\"pt\").input_ids\n",
    "    # Get predictions as distribution over 7 possible classes\n",
    "    outputs = model(input_ids)[0]\n",
    "    # Take argmax to get most likely class per token\n",
    "    predictions = torch.argmax(outputs, dim=2)\n",
    "    # Convert to DataFrame\n",
    "    preds = [tags.names[p] for p in predictions[0].cpu().numpy()]\n",
    "    return pd.DataFrame([tokens, preds], index=[\"Tokens\", \"Tags\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "          0   1   2   3   4   5   6       7       8       9    ... 266  267  \\\nTokens  [CLS]   中   国   网   地   产   讯       7       月      28  ...   积  236   \nTags        O   O   O   O   O   O   O  B-成交时间  I-成交时间  I-成交时间  ...   O    O   \n\n         268 269 270 271 272 273 274    275  \nTokens  ##08   .  32   平   方   米   。  [SEP]  \nTags       O   O   O   O   O   O   O      O  \n\n[2 rows x 276 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>266</th>\n      <th>267</th>\n      <th>268</th>\n      <th>269</th>\n      <th>270</th>\n      <th>271</th>\n      <th>272</th>\n      <th>273</th>\n      <th>274</th>\n      <th>275</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Tokens</th>\n      <td>[CLS]</td>\n      <td>中</td>\n      <td>国</td>\n      <td>网</td>\n      <td>地</td>\n      <td>产</td>\n      <td>讯</td>\n      <td>7</td>\n      <td>月</td>\n      <td>28</td>\n      <td>...</td>\n      <td>积</td>\n      <td>236</td>\n      <td>##08</td>\n      <td>.</td>\n      <td>32</td>\n      <td>平</td>\n      <td>方</td>\n      <td>米</td>\n      <td>。</td>\n      <td>[SEP]</td>\n    </tr>\n    <tr>\n      <th>Tags</th>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>B-成交时间</td>\n      <td>I-成交时间</td>\n      <td>I-成交时间</td>\n      <td>...</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 276 columns</p>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"\"\"\n",
    "中国网地产讯 7月28日，上海2022年第二轮集中供地第四天，共出让5宗地块，总起始价约120.1亿元。\n",
    "福建兆润房地产有限公司&上海铧发创盛置业有限公司（厦门建发&华发）通过一次性报价，以总价9.65亿元竞得上海闵行区1总地块，楼面价40875元/㎡，溢价率 8.00%。\n",
    "地块公告号202205413，地块名称闵行区浦锦街道MHP0-1302单元15-01地块，地块范围为东至：用地红线，西至：浦秀路，南至：江桦路，北至：南江榉路，出让土地面积19673.6平方米，土地用途为居住用地，总起始价约8.94亿元，楼面起始价37846.83元/平方米。\n",
    "据土地出让文件，该地块规划容积率为1.2，建筑面积23608.32平方米。\n",
    "\"\"\"\n",
    "#pd.set_option('display.max_columns', None) # 展示所有列\n",
    "\n",
    "tag_text(text, tags, modelpre, tokenizerpre)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "DatasetDict({\n    train: Dataset({\n        features: ['id', 'tokens', 'ner_tags'],\n        num_rows: 1277\n    })\n    valid: Dataset({\n        features: ['id', 'tokens', 'ner_tags'],\n        num_rows: 426\n    })\n})"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取原始训练据并进行标准化处理\n",
    "import json\n",
    "import numpy, pandas as pd\n",
    "from datasets import Dataset\n",
    "\n",
    "\n",
    "def processLabelInALine(line):\n",
    "    # 初始化空数组\n",
    "    line = line.strip()\n",
    "    if len(line) == 0:\n",
    "        return\n",
    "    linejson = json.loads(line)\n",
    "    nertag = numpy.zeros(len(linejson[\"data\"]))\n",
    "    tokens = [i for i in linejson[\"data\"]]\n",
    "    for (start, stop, tagstr) in linejson[\"label\"]:\n",
    "        if tagstr == \"NN\":\n",
    "            nertag[start:stop] = 0\n",
    "        else:\n",
    "            starttagstr = \"B-\" + tagstr\n",
    "            intenelstr = \"I-\" + tagstr\n",
    "            nertag[start] = tag2id[starttagstr]\n",
    "            nertag[start + 1:stop] = tag2id[intenelstr]\n",
    "    return {\"id\": linejson[\"id\"], \"tokens\": tokens, \"ner_tags\": nertag}\n",
    "\n",
    "\n",
    "def processFile(filename):\n",
    "    f = open(filename, 'r', encoding='utf8')\n",
    "    lines = f.readlines()\n",
    "    processed = list(map(processLabelInALine, lines))\n",
    "    processNone = list(filter(None, processed))\n",
    "    df = pd.DataFrame(processNone)\n",
    "    landDataset = Dataset.from_pandas(df, split=\"train\")\n",
    "    return landDataset\n",
    "\n",
    "\n",
    "#预处理数据，label转化为规范格式。\n",
    "from datasets import DatasetDict\n",
    "\n",
    "land_train, land_valid = processFile(\"train.json\"), processFile(\"valid.json\")\n",
    "dataset = DatasetDict()\n",
    "dataset['train'] = land_train\n",
    "dataset['valid'] = land_valid\n",
    "dataset"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the latest cached version of the module from C:\\Users\\xiaofei\\.cache\\huggingface\\modules\\datasets_modules\\metrics\\seqeval\\1fde2544ac1f3f7e54c639c73221d3a5e5377d2213b9b0fdb579b96980b84b2e (last modified on Mon Aug 15 00:19:35 2022) since it couldn't be found locally at seqeval, or remotely on the Hugging Face Hub.\n",
      "C:\\Users\\xiaofei\\Anaconda3\\envs\\book\\lib\\site-packages\\torch\\cuda\\__init__.py:80: UserWarning: CUDA initialization: CUDA driver initialization failed, you might not have a CUDA gpu. (Triggered internally at  ..\\c10\\cuda\\CUDAFunctions.cpp:112.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset, load_metric\n",
    "from transformers import TrainingArguments, Trainer, DataCollatorForTokenClassification\n",
    "import numpy as np\n",
    "# 定义data_collator，并使用seqeval进行评价\n",
    "data_collator = DataCollatorForTokenClassification(tokenizerpre)\n",
    "metric = load_metric(\"seqeval\")\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "#加载模型对应的分词器\n",
    "bert_model_name = \"bert-base-chinese\"\n",
    "bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)\n",
    "def tokenize_and_align_labels(examples):\n",
    "    #输入字的list，输出用tokenizer的词典中序号表示的字，并对特殊字符和子词进行特殊处理。\n",
    "    tokenized_inputs = bert_tokenizer(examples[\"tokens\"], truncation=True,\n",
    "                                      is_split_into_words=True)\n",
    "    #定义空的标签表示\n",
    "    labels = []\n",
    "    #迭代输入的ner_tags标签\n",
    "    for idx, label in enumerate(examples[\"ner_tags\"]):\n",
    "        #tokenized_input包含word_ids函数，实现对子词与整词的识别。\n",
    "        #这里我们可以看到word_ids将每个子单词映射到单词序列中对应的索引\n",
    "        word_ids = tokenized_inputs.word_ids(batch_index=idx)\n",
    "        previous_word_idx = None\n",
    "        label_ids = []\n",
    "        for word_idx in word_ids:\n",
    "            # 将特殊符号的标签设置为-100，以便在计算损失函数时自动忽略\n",
    "            if word_idx is None:\n",
    "                label_ids.append(-100)\n",
    "            # 把标签设置到每个词的第一个token上\n",
    "            elif word_idx != previous_word_idx:\n",
    "                label_ids.append(label[word_idx])\n",
    "            # 对于每个词的其他token也设置为当前标签\n",
    "            else:\n",
    "                label_ids.append(label[word_idx])\n",
    "            previous_word_idx = word_idx\n",
    "\n",
    "        labels.append(label_ids)\n",
    "    #把处理后的labels数组，设置为tokenized_inputs[\"labels\"]\n",
    "    tokenized_inputs[\"labels\"] = labels\n",
    "    return tokenized_inputs"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/2 [00:00<?, ?ba/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7314a4fe2bd74f6bb08723fd5a77eb29"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?ba/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6908b5abc81f4b0285c8c65421adcb7e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenized_datasets = dataset.map(tokenize_and_align_labels, batched=True, load_from_cache_file=False, remove_columns=['id', 'ner_tags', 'tokens'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "from torch.nn.functional import cross_entropy\n",
    "\n",
    "def forward_pass_with_label(batch):\n",
    "    # Convert dict of lists to list of dicts suitable for data collator\n",
    "    features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
    "    # Pad inputs and labels and put all tensors on device\n",
    "    batch = data_collator(features)\n",
    "    input_ids = batch[\"input_ids\"].to(device)\n",
    "    attention_mask = batch[\"attention_mask\"].to(device)\n",
    "    labels = batch[\"labels\"].to(device)\n",
    "    with torch.no_grad():\n",
    "        # Pass data through model\n",
    "        output = modelpre(input_ids, attention_mask)\n",
    "        # Logit.size: [batch_size, sequence_length, classes]\n",
    "        # Predict class with largest logit value on classes axis\n",
    "        predicted_label = torch.argmax(output.logits, axis=-1).cpu().numpy()\n",
    "    # Calculate loss per token after flattening batch dimension with view\n",
    "    loss = cross_entropy(output.logits.view(-1, len(schemas)),\n",
    "                         labels.view(-1), reduction=\"none\")\n",
    "    # Unflatten batch dimension and convert to numpy array\n",
    "    loss = loss.view(len(input_ids), -1).cpu().numpy()\n",
    "\n",
    "    return {\"loss\":loss, \"predicted_label\": predicted_label}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/14 [00:00<?, ?ba/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "58ee6e58f458498a9d2b4619a9445963"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_set = tokenized_datasets[\"valid\"]\n",
    "valid_set = valid_set.map(forward_pass_with_label, batched=True, batch_size=32)\n",
    "df = valid_set.to_pandas()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "id2tag[-100] = \"IGN\"\n",
    "df[\"input_tokens\"] = df[\"input_ids\"].apply(\n",
    "    lambda x: bert_tokenizer.convert_ids_to_tokens(x))\n",
    "df[\"predicted_label\"] = df[\"predicted_label\"].apply(\n",
    "    lambda x: [id2tag[i] for i in x])\n",
    "df[\"labels\"] = df[\"labels\"].apply(\n",
    "    lambda x: [id2tag[i] for i in x])\n",
    "df['loss'] = df.apply(\n",
    "    lambda x: x['loss'][:len(x['input_ids'])], axis=1)\n",
    "df['predicted_label'] = df.apply(\n",
    "    lambda x: x['predicted_label'][:len(x['input_ids'])], axis=1)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "                                        attention_mask  \\\n0    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n1    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n2    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n3    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n4    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n..                                                 ...   \n421  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n422  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n423  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n424  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n425  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n\n                                             input_ids  \\\n0    [101, 791, 1921, 8024, 1762, 3343, 2336, 712, ...   \n1    [101, 3634, 1912, 8024, 3343, 2336, 1765, 7188...   \n2    [101, 2945, 749, 6237, 8024, 5307, 6814, 122, ...   \n3    [101, 3634, 1912, 8024, 3232, 2128, 1277, 4638...   \n4    [101, 5326, 126, 3299, 128, 3189, 2276, 2255, ...   \n..                                                 ...   \n421  [101, 1343, 2399, 130, 3299, 8024, 704, 3862, ...   \n422  [101, 4012, 3736, 1762, 6821, 3613, 1759, 2864...   \n423  [101, 791, 1921, 4638, 1759, 2864, 704, 8024, ...   \n424  [101, 809, 126, 3299, 122, 121, 3189, 1294, 66...   \n425  [101, 797, 2255, 1277, 677, 678, 2421, 6662, 6...   \n\n                                                labels  \\\n0    [IGN, O, O, O, O, O, O, O, O, O, O, O, O, O, O...   \n1    [IGN, O, O, O, B-地块位置, I-地块位置, I-地块位置, I-地块位置,...   \n2    [IGN, O, O, O, O, O, O, O, O, O, O, O, O, O, B...   \n3    [IGN, O, O, O, B-城市, I-城市, I-城市, O, O, O, O, O...   \n4    [IGN, O, O, O, O, O, O, O, O, O, O, O, O, O, O...   \n..                                                 ...   \n421  [IGN, B-成交时间, I-成交时间, I-成交时间, I-成交时间, O, B-受让人...   \n422  [IGN, B-受让人, I-受让人, O, O, O, O, O, O, O, O, O,...   \n423  [IGN, O, O, O, O, O, O, O, O, O, O, O, O, O, O...   \n424  [IGN, O, B-成交时间, I-成交时间, I-成交时间, I-成交时间, I-成交时...   \n425  [IGN, B-地块位置, I-地块位置, I-地块位置, I-地块位置, I-地块位置, ...   \n\n                                                  loss  \\\n0    [0.0, 0.05903319, 0.015438806, 0.0017415609, 0...   \n1    [0.0, 0.004652034, 0.001520907, 0.0013681822, ...   \n2    [0.0, 0.0016457598, 0.0011335146, 0.0009338070...   \n3    [0.0, 0.005207071, 0.0016787258, 0.0012785364,...   \n4    [0.0, 0.011732151, 1.7508378, 1.7859567, 1.431...   \n..                                                 ...   \n421  [0.0, 0.23609744, 0.08170999, 0.09645948, 0.03...   \n422  [0.0, 0.40085357, 0.1841239, 0.000887596, 0.00...   \n423  [0.0, 0.06985708, 0.010708016, 0.0014791273, 0...   \n424  [0.0, 0.007060222, 0.09641975, 0.021977214, 0....   \n425  [0.0, 1.266073, 0.15174387, 0.14995341, 0.0947...   \n\n                                       predicted_label  \\\n0    [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...   \n1    [O, O, O, O, B-地块位置, I-地块位置, I-地块位置, I-地块位置, I...   \n2    [O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-受...   \n3    [O, O, O, O, B-地块位置, I-地块位置, I-城市, O, O, O, O,...   \n4    [O, O, B-成交时间, I-成交时间, I-成交时间, O, B-城市, I-城市, ...   \n..                                                 ...   \n421  [O, B-成交时间, I-成交时间, I-成交时间, I-成交时间, O, B-受让人, ...   \n422  [O, B-受让人, I-受让人, O, O, O, O, O, O, O, O, O, O...   \n423  [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...   \n424  [O, O, B-成交时间, I-成交时间, I-成交时间, I-成交时间, I-成交时间,...   \n425  [O, B-地块编码, I-地块位置, I-地块位置, I-地块位置, I-地块位置, I-...   \n\n                                        token_type_ids  \\\n0    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n1    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n2    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n3    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n4    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n..                                                 ...   \n421  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n422  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n423  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n424  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n425  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n\n                                          input_tokens  \n0    [[CLS], 今, 天, ，, 在, 杭, 州, 主, 城, 区, 地, 块, 的, 角,...  \n1    [[CLS], 此, 外, ，, 杭, 州, 地, 铁, 4, 号, 线, 二, 期, 工,...  \n2    [[CLS], 据, 了, 解, ，, 经, 过, 1, 6, 2, 轮, 竞, 价, ，,...  \n3    [[CLS], 此, 外, ，, 晋, 安, 区, 的, 另, 外, 两, 宗, 地, 块,...  \n4    [[CLS], 继, 5, 月, 7, 日, 岱, 山, 县, 拍, 出, 我, 市, 今,...  \n..                                                 ...  \n421  [[CLS], 去, 年, 9, 月, ，, 中, 海, 就, 以, 6, 9, ., 7,...  \n422  [[CLS], 滨, 江, 在, 这, 次, 土, 拍, 中, 发, 挥, 颇, 为, 稳,...  \n423  [[CLS], 今, 天, 的, 土, 拍, 中, ，, 多, 个, 地, 块, 在, 到,...  \n424  [[CLS], 以, 5, 月, 1, 0, 日, 卓, 越, 在, 首, 拍, 中, 拿,...  \n425  [[CLS], 仓, 山, 区, 上, 下, 店, 路, 西, 侧, ，, 上, 下, 店,...  \n\n[426 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>attention_mask</th>\n      <th>input_ids</th>\n      <th>labels</th>\n      <th>loss</th>\n      <th>predicted_label</th>\n      <th>token_type_ids</th>\n      <th>input_tokens</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 791, 1921, 8024, 1762, 3343, 2336, 712, ...</td>\n      <td>[IGN, O, O, O, O, O, O, O, O, O, O, O, O, O, O...</td>\n      <td>[0.0, 0.05903319, 0.015438806, 0.0017415609, 0...</td>\n      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 今, 天, ，, 在, 杭, 州, 主, 城, 区, 地, 块, 的, 角,...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 3634, 1912, 8024, 3343, 2336, 1765, 7188...</td>\n      <td>[IGN, O, O, O, B-地块位置, I-地块位置, I-地块位置, I-地块位置,...</td>\n      <td>[0.0, 0.004652034, 0.001520907, 0.0013681822, ...</td>\n      <td>[O, O, O, O, B-地块位置, I-地块位置, I-地块位置, I-地块位置, I...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 此, 外, ，, 杭, 州, 地, 铁, 4, 号, 线, 二, 期, 工,...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 2945, 749, 6237, 8024, 5307, 6814, 122, ...</td>\n      <td>[IGN, O, O, O, O, O, O, O, O, O, O, O, O, O, B...</td>\n      <td>[0.0, 0.0016457598, 0.0011335146, 0.0009338070...</td>\n      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-受...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 据, 了, 解, ，, 经, 过, 1, 6, 2, 轮, 竞, 价, ，,...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 3634, 1912, 8024, 3232, 2128, 1277, 4638...</td>\n      <td>[IGN, O, O, O, B-城市, I-城市, I-城市, O, O, O, O, O...</td>\n      <td>[0.0, 0.005207071, 0.0016787258, 0.0012785364,...</td>\n      <td>[O, O, O, O, B-地块位置, I-地块位置, I-城市, O, O, O, O,...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 此, 外, ，, 晋, 安, 区, 的, 另, 外, 两, 宗, 地, 块,...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 5326, 126, 3299, 128, 3189, 2276, 2255, ...</td>\n      <td>[IGN, O, O, O, O, O, O, O, O, O, O, O, O, O, O...</td>\n      <td>[0.0, 0.011732151, 1.7508378, 1.7859567, 1.431...</td>\n      <td>[O, O, B-成交时间, I-成交时间, I-成交时间, O, B-城市, I-城市, ...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 继, 5, 月, 7, 日, 岱, 山, 县, 拍, 出, 我, 市, 今,...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>421</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 1343, 2399, 130, 3299, 8024, 704, 3862, ...</td>\n      <td>[IGN, B-成交时间, I-成交时间, I-成交时间, I-成交时间, O, B-受让人...</td>\n      <td>[0.0, 0.23609744, 0.08170999, 0.09645948, 0.03...</td>\n      <td>[O, B-成交时间, I-成交时间, I-成交时间, I-成交时间, O, B-受让人, ...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 去, 年, 9, 月, ，, 中, 海, 就, 以, 6, 9, ., 7,...</td>\n    </tr>\n    <tr>\n      <th>422</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 4012, 3736, 1762, 6821, 3613, 1759, 2864...</td>\n      <td>[IGN, B-受让人, I-受让人, O, O, O, O, O, O, O, O, O,...</td>\n      <td>[0.0, 0.40085357, 0.1841239, 0.000887596, 0.00...</td>\n      <td>[O, B-受让人, I-受让人, O, O, O, O, O, O, O, O, O, O...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 滨, 江, 在, 这, 次, 土, 拍, 中, 发, 挥, 颇, 为, 稳,...</td>\n    </tr>\n    <tr>\n      <th>423</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 791, 1921, 4638, 1759, 2864, 704, 8024, ...</td>\n      <td>[IGN, O, O, O, O, O, O, O, O, O, O, O, O, O, O...</td>\n      <td>[0.0, 0.06985708, 0.010708016, 0.0014791273, 0...</td>\n      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 今, 天, 的, 土, 拍, 中, ，, 多, 个, 地, 块, 在, 到,...</td>\n    </tr>\n    <tr>\n      <th>424</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 809, 126, 3299, 122, 121, 3189, 1294, 66...</td>\n      <td>[IGN, O, B-成交时间, I-成交时间, I-成交时间, I-成交时间, I-成交时...</td>\n      <td>[0.0, 0.007060222, 0.09641975, 0.021977214, 0....</td>\n      <td>[O, O, B-成交时间, I-成交时间, I-成交时间, I-成交时间, I-成交时间,...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 以, 5, 月, 1, 0, 日, 卓, 越, 在, 首, 拍, 中, 拿,...</td>\n    </tr>\n    <tr>\n      <th>425</th>\n      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n      <td>[101, 797, 2255, 1277, 677, 678, 2421, 6662, 6...</td>\n      <td>[IGN, B-地块位置, I-地块位置, I-地块位置, I-地块位置, I-地块位置, ...</td>\n      <td>[0.0, 1.266073, 0.15174387, 0.14995341, 0.0947...</td>\n      <td>[O, B-地块编码, I-地块位置, I-地块位置, I-地块位置, I-地块位置, I-...</td>\n      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>[[CLS], 仓, 山, 区, 上, 下, 店, 路, 西, 侧, ，, 上, 下, 店,...</td>\n    </tr>\n  </tbody>\n</table>\n<p>426 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "  attention_mask input_ids labels  loss predicted_label token_type_ids  \\\n0              1       791      O  0.06               O              0   \n0              1      1921      O  0.02               O              0   \n0              1      8024      O  0.00               O              0   \n0              1      1762      O  0.00               O              0   \n0              1      3343      O  0.48               O              0   \n0              1      2336      O  0.73               O              0   \n0              1       712      O  0.05               O              0   \n\n  input_tokens  \n0            今  \n0            天  \n0            ，  \n0            在  \n0            杭  \n0            州  \n0            主  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>attention_mask</th>\n      <th>input_ids</th>\n      <th>labels</th>\n      <th>loss</th>\n      <th>predicted_label</th>\n      <th>token_type_ids</th>\n      <th>input_tokens</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>791</td>\n      <td>O</td>\n      <td>0.06</td>\n      <td>O</td>\n      <td>0</td>\n      <td>今</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1921</td>\n      <td>O</td>\n      <td>0.02</td>\n      <td>O</td>\n      <td>0</td>\n      <td>天</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>8024</td>\n      <td>O</td>\n      <td>0.00</td>\n      <td>O</td>\n      <td>0</td>\n      <td>，</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1762</td>\n      <td>O</td>\n      <td>0.00</td>\n      <td>O</td>\n      <td>0</td>\n      <td>在</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>3343</td>\n      <td>O</td>\n      <td>0.48</td>\n      <td>O</td>\n      <td>0</td>\n      <td>杭</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2336</td>\n      <td>O</td>\n      <td>0.73</td>\n      <td>O</td>\n      <td>0</td>\n      <td>州</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>712</td>\n      <td>O</td>\n      <td>0.05</td>\n      <td>O</td>\n      <td>0</td>\n      <td>主</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tokens = df.apply(pd.Series.explode)\n",
    "df_tokens = df_tokens.query(\"labels != 'IGN'\")\n",
    "df_tokens[\"loss\"] = df_tokens[\"loss\"].astype(float).round(2)\n",
    "df_tokens.head(7)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "            0      1       2       3       4       5       6      7       8   \\\nlabels  B-地块位置  B-容积率    B-城市  B-土地用途  B-出让面积    I-城市  I-出让面积  B-起始价  B-地块编码   \ncount      164     32     156     117      84     548     514     37     254   \nmean      1.26   1.02    0.95    0.85    0.78    0.53    0.52   0.51    0.51   \nsum     206.66   32.8  148.26   99.99   65.77  291.27  267.64  18.94  128.56   \n\n            9   ...      15      16     17     18     19     20      21  \\\nlabels  I-土地用途  ...  I-成交时间  I-地块编码  B-触发词  B-成交价  B-受让人  B-溢价率       O   \ncount      513  ...     692    3594    469    357    462    200   26983   \nmean      0.49  ...    0.21    0.19   0.17   0.17   0.15   0.12    0.11   \nsum     251.05  ...  143.06  675.21  80.83  59.04  71.52  24.44  2927.1   \n\n            22     23     24  \nlabels   I-受让人  I-成交价  I-溢价率  \ncount     2540   1979    749  \nmean      0.08   0.05   0.03  \nsum     204.04  95.35  24.81  \n\n[4 rows x 25 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>labels</th>\n      <td>B-地块位置</td>\n      <td>B-容积率</td>\n      <td>B-城市</td>\n      <td>B-土地用途</td>\n      <td>B-出让面积</td>\n      <td>I-城市</td>\n      <td>I-出让面积</td>\n      <td>B-起始价</td>\n      <td>B-地块编码</td>\n      <td>I-土地用途</td>\n      <td>...</td>\n      <td>I-成交时间</td>\n      <td>I-地块编码</td>\n      <td>B-触发词</td>\n      <td>B-成交价</td>\n      <td>B-受让人</td>\n      <td>B-溢价率</td>\n      <td>O</td>\n      <td>I-受让人</td>\n      <td>I-成交价</td>\n      <td>I-溢价率</td>\n    </tr>\n    <tr>\n      <th>count</th>\n      <td>164</td>\n      <td>32</td>\n      <td>156</td>\n      <td>117</td>\n      <td>84</td>\n      <td>548</td>\n      <td>514</td>\n      <td>37</td>\n      <td>254</td>\n      <td>513</td>\n      <td>...</td>\n      <td>692</td>\n      <td>3594</td>\n      <td>469</td>\n      <td>357</td>\n      <td>462</td>\n      <td>200</td>\n      <td>26983</td>\n      <td>2540</td>\n      <td>1979</td>\n      <td>749</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.26</td>\n      <td>1.02</td>\n      <td>0.95</td>\n      <td>0.85</td>\n      <td>0.78</td>\n      <td>0.53</td>\n      <td>0.52</td>\n      <td>0.51</td>\n      <td>0.51</td>\n      <td>0.49</td>\n      <td>...</td>\n      <td>0.21</td>\n      <td>0.19</td>\n      <td>0.17</td>\n      <td>0.17</td>\n      <td>0.15</td>\n      <td>0.12</td>\n      <td>0.11</td>\n      <td>0.08</td>\n      <td>0.05</td>\n      <td>0.03</td>\n    </tr>\n    <tr>\n      <th>sum</th>\n      <td>206.66</td>\n      <td>32.8</td>\n      <td>148.26</td>\n      <td>99.99</td>\n      <td>65.77</td>\n      <td>291.27</td>\n      <td>267.64</td>\n      <td>18.94</td>\n      <td>128.56</td>\n      <td>251.05</td>\n      <td>...</td>\n      <td>143.06</td>\n      <td>675.21</td>\n      <td>80.83</td>\n      <td>59.04</td>\n      <td>71.52</td>\n      <td>24.44</td>\n      <td>2927.1</td>\n      <td>204.04</td>\n      <td>95.35</td>\n      <td>24.81</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 25 columns</p>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df_tokens.groupby(\"input_tokens\")[[\"loss\"]]\n",
    "    .agg([\"count\", \"mean\", \"sum\"])\n",
    "    .droplevel(level=0, axis=1)  # Get rid of multi-level columns\n",
    "    .sort_values(by=\"sum\", ascending=False)\n",
    "    .reset_index()\n",
    "    .round(2)\n",
    "    .head(10)\n",
    "    .T\n",
    ")\n",
    "(\n",
    "    df_tokens.groupby(\"labels\")[[\"loss\"]]\n",
    "    .agg([\"count\", \"mean\", \"sum\"])\n",
    "    .droplevel(level=0, axis=1)\n",
    "    .sort_values(by=\"mean\", ascending=False)\n",
    "    .reset_index()\n",
    "    .round(2)\n",
    "    .T\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "def plot_confusion_matrix(y_preds, y_true, labels):\n",
    "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
    "    fig, ax = plt.subplots(figsize=(30, 30))\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
    "    plt.title(\"Normalized confusion matrix\")\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2160x2160 with 1 Axes>",
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tHG7OIDkHZwDOAJxBxTk4A3AG4AwqzsEZgDMAZwCjJ4PhrneY9PJk3r/fyWy83tu44dcP8YNj9+DyG+/nyAkf6HeZ68/+DMAszy00jDmoj97PQ2W0lIXh5AycQcU5OANwBuAMKqMlh0XW+9SwrXvB+eflmjMP4ea7/8AWG6zKPkf+kO3Gv4PjzrhqpuWO2G8b/vzEs1x47d0ArL3qcnz7Sx9jmSUX5e4H/sxuh5/Jvyf/Z9i2c8r937k3Itbt67W2bsiStDzwBeCXwN+BA4DrgQ3J+2VNAc4n72+1K3AoMA85DeFhwK3AZyPir7V1HgDsA2wHTImIZ8rzywLHA6dFxK/726bhbMgCeGnSK/zqrkfY4B0rscS4hZpeppn3jRbOIDkHZwDOAJxBxTk4A3AG4AwqzsEZgDMAZwCjI4NW1DuMhhyGsyELRkcGw80ZOIOKc3AG4AzAGVRGQw7D2ZAFsPCCY9ls/VW4477HeOb59rzfV9s2ZDUiaTwwOSLukPRJ4GlgUeB24PfAOGDziLhA0hjgEnKKwAuAn5IjriaQUxNuERHPSVqcbMCa1OuzJgDzAadExLT+tmm4G7LMzMzMzMzMOkk71zu00nA3ZJmZmdnoNdwNWaPBQA1ZY1q9MYMRETfW/v5uH4s8QzZaERHTJO0YEZNrr18LXCtpjoiYUZZ7pp/POn3ottzMzMzMzMzMzMzMzMxm1xwjvQFDqVcjVv35Ga3eFjMzMzMzMzMzMzMzM5s9HdWQZWZmZmZmZmZmZmZmZp3DDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm3JDVlmZmZmZmZmZmZmZmbWltyQZWZmZmZmZmZmZmZmZm1JETHS2zCqSHoW+MsIb8Y44LkR3oaR5gycQcU5OANwBuAMwBlUnIMzAGcAzqDiHJwBOANwBhXn4AzAGYAzAGdQcQ7OAJwBtEcGy0XEYn294IasUUjSPRGx7khvx0hyBs6g4hycATgDcAbgDCrOwRmAMwBnUHEOzgCcATiDinNwBuAMwBmAM6g4B2cAzgDaPwNPLWhmZmZmZmZmZmZmZmZtyQ1ZZmZmZmZmZmZmZmZm1pbckDU6nTnSG9AGnIEzqDgHZwDOAJwBOIOKc3AG4AzAGVScgzMAZwDOoOIcnAE4A3AG4AwqzsEZgDOANs/A98gyMzMzMzMzMzMzMzOztuQRWTZiJI2TtPbreN+ikuZuctmFGrw+d7PrGi7OoTUZjAbOoTmS1MQyc0qas8Hrc/R67i2S5hmKbWwF5/D6vzODWP8KA+XXLro1B0mrNNqu0VSeXy/nMDBJ80haoPZYksb0s+yY+nKt2L5WcQ5mPVzGk3OwblR+/+apP34d6xjbjufGzXIGs5I0b4PXN23Nlows52Dtzg1Zo4ikBSRdKul2ST/q7+JzFHkrsDeApLnKf+/ovZCk90g6tvbUfsDWfSy3gaSTao/nAe7stcxOkq6VdJ2ka4BPA5dJuqrkeuJQ7NggOYchzqAse5qkxcrf+0p6dx/LnCzpXkkPSrqv/P2opNskXTUE+zVYXZ2DpGVr27qPpH3K34tLWqK26BclfbD2vtUl/bTX6rYCJkqq/r0k6ZbqMTARWEfSOyVdKOl3wLHAOyXdUPt3wfDtcd+cw8wkHSPpE/283Ox3Zi9JD5V9eVjS78rf90o6pbbcN6uTd0lvBC4Hjh7qfXo9nEOf9gK27/3kYMpzlVf5+2BJO9Uej5bzrK7NQdKutWPkepI2LP/eWVtsDeCE2uOtge/1s8pvq+cC/cOSThvqbR4OzmFwJO0iadwAr3dF5X6n5SBp99pv4EmS5it/nyBp1V6LNyzjo/i46BwG0KjcN7mON0raufw912j4fvTWDTlIOlDSsZK+JelMSWdL+hlwM7BvbdFmrql6OxrYc+i3emg5g75J+ryk8b2evkjSOyRd1E9Z3queUSdwDo1JGlv+O0v7iaTxkhZW0fqta412y8ANWaPLgcCjEfEeYB7goyO8PX2SdI6yMnyisgKlv14aU4FqbsurlCNKXiknQevXviSvAtMkLahsoHkfsKuk4yVtWVvfTsBFZRseBH4BPFO24xlJ80bEBWSOD0fENhFxAvkjvj3wdeDRoUsCJD3WxGIdm8NIlQVJKwDjgRfKwfT+sm9V76PqADvQ3KpDOu/qSJSFdslhEOXgI0DV0Dat/IOsbHt/bblvArvU8phKZtKz0RFXR8RGEbEpsDlwb0RsHBGbln8bRcTdwAPAAcCfI2JX4PfA3yJii4jYAlhqtna+lybLQcfmMIiyUDfLfvV6rZnvzDTgpLIvpwDfKH9/BvhPbX1zAVtLWhA4B9gd+Ivyom8kykJdR+ZQabZsKBvYq4bY9YFPqaeh9tHyvsGU5ztUGneBg4H9y7puBn5dnbi3UjNlo5NzGERZuAg4DDhQ0tHAWGAhYEHgO2WZhYC1gEfV0xFgd+Crkv5b0iK19c0HrA3coqy4nQCMk7TZsOxoE5osCx2fw2BIOkXS2spOf2/oZ7ENgY8PsJpR35DXSTkM4txhHHBlKbe/Bd4taTlgNeCR6rdwEGW8bY6LlSbPHTo+h/4MUbnva70HSZpQe+pl4HhJawA/Bm5UTyeRFwf47Jbo9BwGcUy4G3gzcCrwN+A4YFdgCvCz2nINr6n6MI3c/xHT5PGgozPobRBlYyowXdJ7e51PnwqsAGwsaYKkuyXdXF5fBTi8Ov6V1/ZqxX69Hk2Wj47PoU7SUpIuk7SkejptbqnSGay2nNQzOOBkSVuQ11r79VrlAsCXyM5i1ygHBlT/HpH03uHep8HqhAxGXQ+aLrc+cFb5+zZgPeD8kducAR0YEbdJOoOsZL+2/qKkDwFfAJaU9CJ5AN0FWB24kDypfoukHckGl3HA3MCXgf2BB8kf1o2BX0h6M7AEWbn/ceBFsvKtMoGeSr5NgNtrr20SESdIWoYhbshqpEtyaGlZKKs9BLiSbNTbi7LPkm4AVF5/gMzmB+U965IneZeVxzOGYueb1QU5DFgOiun9fN70sr1Vr9AZwMciYpZly+vTIiIkfZVsEJoXWKHsd+WKiDiVPI6uQ2b7aWYt+yNxI8lOz6GZstBQs9+ZsniQJ9wfA5YGZkjaA3gDcF1ttV8B5geuB/5NVvQKWAa4QtLnIuKm17O9w6XDcmimbPyRPJ7tS54TzUn2CD2ZvDiHwZXn64D/JY+pKwJ/Ap4A3gSsGBGTZ3+3hkWn59CwLETERyR9GFggIs6pvybpc+XPTYGDgIeBCZL2BG6LiMclrQx8F9i5LPsp4KGImKGssP8u+Rt6maTFIqJe4dM2OjUHSecAawL/BP4B7BIR05t4azUlzjnA7pJOJSvwTyWPZ5DHt+UlbV4eLwx8OiLuUU/l/ifVU7n/D0mbRcSvZn/PBk/SYxGx0iDf1mk5NLqeGAN8C/g5uc+LkudNkMe6K8vzF9F8GW+342JDnZLDSHz/gfmAn5K/lW+PiPosCNMpHX6UFeOLAocCT0fEzrXlUFb0NmoAaFqrv/+0aQ59aOY84c7yewdZf/YOYFvg1oh4pmxn09dUvV6an+ww0ta6NINGvxdbAtuR18c3AOdExDmSvk026E0HnoyIm4HTa+87BHip97nWaNVJOTTzm6GcXvs08vr2erKRbnNgM+CNyj7eP4qIqWTnr8rbgVuAXwFnS/pJRLwCEBGXS/ob8HfgDHp+byeR9XJTh3xnB9Do96JTMnBD1uiyID0nHq+QvS3b3Th6trnuMuCvZOXYy2Sjyq+AxyJiB0kTyw/lhZL+CHwgIr4MIGkG+UM7nZ5KmA2ByWTD3lbAPuSUWZWJ9PQ235GsvPtc+dzqBGsZoNUVlJfRPTm0pCxIehvZy+i0iDhP0rSIuLC89gXggoj4U/nszenJbnPgKWCL8vgV4KEh2vdmXEZ35NBfOYBsmPuapMOAJcu27kE2zn6rLLMeWVm7MLBB7b3vKw00Y4AdgBeAVclGvGOBD5ANdB8ErqJnGoQnyNHJK5JTcNZHpYyUbsmh37Ig6UZgEbL8LQO8quwlNC/wRERsT/PfGcjy/Y2IOEvS/sCUcqK+KXmsRNLu5IXdccB/l/e9izyJ2xl4vo8LuGHVxTkMdJw4H7gGOJucVvVF4GrgYuD0iJguaTDl+X+A5cnv0/zAY+Tv4zZkR4F21S059FkWJK0FnFRen7NU0lxMltHp5LGRiLhC0i7k9+NS8hi6kqRPksfHBSTtQHYG2RW4T9KPgMeBiRHxqqSvAQdIGhMRbdWBrAtyaFhRKemHwEr0lJMVyePVS+QMFhdGxAPAZsqG/3dHxGHlvWcAd0TEj6XXRqm3RUPeYHVRDv39PqxCduo4jZxd4mByROJ/yIr2m4BLlA23zZbxdj0uDqSTcmj1938D4NKI+JR6RhisRo46WZO8bn6V/B78PCLeI+kXmnnEx1blv60+X+zmHPo7T1iCPB9+ATi39tLSwNPKKTK3IRtkm72mqvuv8p7vD81uDD1n0HfZiIhfSFoduAdYjBydvhtwK3nd9SNKJX55vprafWmyw3g1cuXEiLhsOHdgOHVgDo1+M7Yj61P2IDtxHkj+Zl5K1o9sQ15fTQUOB9aUdBN5/Lumtp7rlB3INiPrzH4J/IWR6QA9WB2RgRuyRpdJ5LA9yJPISSO4LY2cpuwR9i+yF9BXaq/dFBFfkVQV8h8CG0fENyX1eZ8fAEnHkcMVVyAr9V4hW43/GRHfkvQKcC9Zofdu4BKyl9C8wLPkcMjfkBX165LTsMwDvLX8QC8GvFfS4RFx4xDlUN/+7wFvqz3VLTm0tCyQlXZfBt5YFt9X0p1kHp8ge5cAUPK6jp5eawuR/4/GRMSGs7PTA2lFWaD9cmhYDoC/RsTqZd/2KNt2TnnfNuXxHZK2Z+bRIwC/jIg9ej1X/yG9iiznl9dfi4g/S3oXmdengSOArZS9CCEbD4ZFf+WAzs+hYVmIiNfm6pZ0FDkd2jn1lZSK+ma/Mw3PdyLiR5LeQp7U7UWe1C1Mfh82JLPast8VzIb+ykK35UBzx4nHyClStyF7ec1PjiAbT16wDrY8r0JWyB1Kjm49k7wgf4nM4Oyh3snBGOA40ek5DFgWgK+SlbBLkzl8idz2YyKi93FxAXIfngA+BrxYNcYq7yM6gxx5cgSZwRFkjtuS36njI2KDWgX/iOirLNA9OQzUuB3AvhHxCICkA8jpkx4E1oyIp8s2Hw58iBxZVLkEOFTS+hFxwCAr99tNp+fQzPXE3uTUmtWUSNW5rsiyfQNZOddsGW+34+JMBrie6LQcWvX9nw5sL2k1YPGIOFvSMhHxhHo6//xEORVT1cN8THWupuw4NG2Efiq6MYdG5wnfB/6PPJbNqH7vKsoRHGMGeU1VvXdh8tw4JC0eZWTTSOrnHKGrMqhp5nqiMjdwDNlQ9zjZee+R6BnNsxhwVrkWP4QyEqn8vQijxADXE5VOyqG/BszzlJ22riMbXXYlO3k8S15HvULPrDifKI8/CiwWOYNNbxdKehLYMiIukvQBYOXy2k7k9Vlb6ZQM3JA1utxFTg1yJbAR2WrarqrW8JPJ3tt797dgRDxTq4SrKm57HwyXBH4dEUdKurg83jrKcMZS6fZ1cnjkfmSPoU+SvYGWJ6dXeAM5lPq82noXJr9gkBU9ezJM8/xGxCcavN6pObS0LJT3bUjPiKJzyX27uryv9xQHk+iZRq+yFcOoFWWhvK+dcmimHEwALujjeZE9Sq+Yjc8/nWzUm2kkq3LqnE+S94i7g+yJsglZSbwww9hhYIBy0Ok5NH1MaFYT35n5yem0diJ7Hc5QTq+3CHm8nGl1ZG/mXch7Cq1LVgYM229uo2PCINYzqnOgubLxKnBi2ZadydHqx5MVbOdLupU8IW9YnstF+CfI/fxBee0zZMXMUcDOkuaOiBEbqTlA2ej0HBqWBUnfIXuLjifv6/Ys5f6/pbLtIXIKxsXIi7AbyFGpR0v6R1nNihGxFHCX8qb3O0bEU+WY+ERZppo6Y0R7WvZXFjo8h2YqowI4r1TS3kdeJ60AvJMs65DXvFPJht/bJK1PniN/k2yYr+7xM5jK/RExQGVUp+fQzO/DVuR0queTo9E3BR4hK2z2iIingKeaKeNtelycyQC/D52SQ6u//9PpGYl0p/LeX1dKGqgibhX1TNm95mzub0Mt+v63fQ7FgMcE5b2e9iI7fBws6b7y0hrAe8mpmae9zs8+CPgJ8CR57rXX61zPkOnreNBtGdQ0e625EdmRdyJ5nX0f2bB7fG2ZgW6zMBpG4AANrzU7JYdmfjMWJu9HPoms7xxLfhfGkVPxCyAi/qUcZXoEMFlSNUvJfMC2EfF8bd1VY9+iEbGppBNpg7aWAX4vRn0GIx6uDcq3gXMl3UHOWXzRCG9PM16k/3lz5yaHKp5Qe24ecsTI9gCSPkqeLP8W+INyCpUpwBfJ+0BUN5qbTvY4uaUsfyk5BUY1Emkd4ITSo2gFcu7O5cibkq5IHnznIr/o1Ul8q3RLDq0qC71dTfZQ3pKZh8NW5qWnEa/S0vtj1XRDDgOVg/5OgqKv1yR9ltp8zQ38ieyZs1qv53cnvyO7RsTJyl7pt5PDrj9KfneObvIzhkq35DBQWWhWw+9MsSqwf7mo6XNKvV6+RlYCjCUzXwC4WNIOI1lpNYBOy6HPsqGcGu3j5BS665PTRkAev14i93UqeRLeTHn+UFm26k2+WPncJ4FTyN/D5+i5P2lb6LIcBjpOLEdeiN0YEUdJ2hY4pRwTVwDeExFPKm9Mfwt5X8hXyPOgswAk3dXPut9CNv6NBp2cQzOVUWPJHsSLk2X5t2QD7tzkuRGRc/1/C6BcXH+dLFdHR8TfqxVFRLs15M1igMqobslhoGPCB8h7As0/iPX1V8bb+bjYSKfk0NLvP6UDQKGImFw6Cqw3wDY+HBFblHVPHPQeDlIrvv+Mghx66fOYEHmvp1eAL5QG2Z+T10AHR0Sfv3nNXFNJ2gDYGtgoclaEPSUdHBGnzOZ+DDln0O/1xG7k9l9KjlC/j5xu7R/AsuS951eMiD8y8/ehev9CZKX/c8O36cOvA3No5jdjofJvCnA/eY/hZ8t/H2TmupajyY5gT0TEwZKq6+jnaWyWvFptgN+LUZ+BG7JGkYh4mZkrpNrZacop7iB7c89E0jbk0PYLydb+0yUtC+wTEb+X9EHlvP0nAFdHxL8lvYNszNuh9Dp/j6SzyEr9Ock5ez9D9j4bT84NvjXZKHMuORIJ8oRuf3pucvnL0tK8EHni9/QQZ9GvLsmhpWUhIv5J6UUg6Tyyggey0u7vkvYjL+7OLsu9yqzD6DcoWUT0ccPT4dAFOQxYDoplahc/S5Zt36NsX/0mkfOSw93vptc9X0pFLdEz4uxispF2FbJifzHyAv/Ostzp5X0fK8t/A/hmqfQ7FbhV0rUR0cqKvE7PodExob8bClevz0Ee695Lc9+Zw8mcDq9WUf5BngdNr61+BbJy/0HyxubXAxMi4m5Jz9XeN+y6NIcBy0bkNFbnSzoGOC4i7gGQtDxwWER8qr58E+X5bGpTIimnx3g6IvoaEdk2uiSHRseJ1cnfrAWANSQtHBFXkg2v1TLVBdTe5NQZy5Nl/dO1TJaorbYaxbQEORXGkUO2N8Oki3IYqPFiWeB5ciqTF8gpcdYEdutn+ZvIe/78kVnPe3ob6Ya8wej0HBodE+Yjp7/dl7zumYVyOsRpNFHG2/S42FCH5tCq7/8YeqbUexNARHwfQNJb+1nfWqqNRFKOBhgJ3ZhDM9eWlWPIjjxzAZv18XpT11TKGQ2OALaJninXdiPvF7MueQ72D9pTN2XQqGz8LCLOlXQQcCM5cv3rZIPOUeSAgXvJxuBJ5DEIeq5/9iHvD3ba8Gx+y3RqDgP9ZjxLdvJ7nByh/GFlh87lye/F/MCUksnqZL3oVyUdSt4/rvfIQwErS/oisGQ5Di5P852MR8Koz8ANWTbkop95dHstcw21USGSfkJOiyLlTBWvAJ+LiGfL63ORPUh3ijL/buQc4AcD7ydP3P4F7F0q+LcjD2C/AG4je6r+oLzvs+Vj769t0hvL5393KBsuImKlBq93dA4jVBbOJ3ulzQ3sXi7UZlIaZ6rK32nklBu9XUfeHPmyZva1kREoC22TQzPloCy3Yl/Pl4uh+rRni5I/jLeT0+xNLctdRZ6cn0k23IwBPhQRz/Va31rkBX7dfJLWIEeo/LRsz/TSALAaQ1SB06gclGU6Nocmy8KRwNaqTZFXtnf/6k/g8oj4H5r4zpAnZHdHz7Sbk4CppdL/RHK6xmq048vkNL47ko2+/6FnmoIFyanZvj2one5HE2WhK3KoNHucKOYEvifpX+XxvOQ9AHobbHmehzY4N27mOFF0ZA5NloX1yZ7F95Mjka9U3q9jDvL4twhwuaRrgf9ExOUAkj4OfCN6RiL9prbOar+/Qf7eVudBM03F2kpNlIVOz6FR40XVq3R9cnqso+mZmWBbYPGyzEXkiPRNyYvrLclptu9U3j/0LvJ4+gLt2ZA3oE7PocljwqpkA+4Y8p6AU8rzq5R/c5BTN5/G6yvjo+X3oZNyaOn3n/xNvTRySr0v9f642t9zVI8jYlwf2zU3LZzdoxtzaOaYoOzEcRrwdrJx7xCy4+bxytsxTCGnx/onTVxTSfod8EFgfNRGI0TEJEmbk8fIceRolpZodDzohgx6a7L+qero+TBwekRMVY402Sciflf28+GybH206VzA3BFxEnDS0G750Gui/qnTcmj0m7E8OZr4ZfLadi5lZ+ENyQ6/U4BtJP0BWIm8Dp6PrGM9DLiVHBRQv+XCGuT36wjgjKpeTtmpdFWygbBtdEoGipGdHcGsJSTNWesx0rWcg5l1IzVx/4bS6jMmcuqV+nNzlYfTR/vx0zlYtyuNsfTVuaObdEoOkj5HjiY9g+wVfCJ5EX2ecjT6VuQotD+RHTd+HrXplEqnoPeRF/BfLJU4y5DTqs4AzoyI28qy/xsR72zd3jXPOVg3GupyT57nLNh7NImkjwBfISt3b5e0OHBFRLyrj206D3hrK78jzqF/khYFXuqrg66kOYayA3O7cgZmSdJ4YHJE3FE68z1NNuDeTt4PehyweURcUM6TLyEb7C8AfkoeJyeQI2C3iIjnynFwSkRM6vVZE8gGoFPa6Vy7UzJwQ5aZmZmZmZmNKqUCltLwImDJiHiq9vrKEfGHEdvAFnEO1o1aVe4ljQVm1EYvDLTsghHxr0bLDSXnYGY29CSNjYjJfTzfNQ3A7ZqBG7LMzMzMzMzMzMzMzMysLc3ReBEzMzMzMzMzMzMzMzOz1nNDlpmZmZmZmZmZmZmZmbUlN2SZmZmZmZnNJklflvSwpFsk3Shp6dlYz6a1xyc38Z61JK31ej+jn2Umzu46BrM+MzMzMzOz/rghy8zMzMzMbGgcFxEbAz8EDhyKFUbEIU0stlb5Z2ZmZmZm1nHGjPQGmJmZmZmZdZhFgMnw2kiku4E1ImJLSfMBPwYWBx6IiAMkLQJcBMwJCJhYrUjSxIjYtPw9L3AO8GbgJeCjwNHA9uX13SJi/GA/oxFJCwA/A+YF/hIRe5aXDpV0DPAUsCswAzgTWBl4FtgxIqY3+zlmZmZmZmZ98YgsMzMzMzOzoXGkpFuAdwGnlOfeBfw6IrYsj/cDHiwjt5aStEZ57qqI2AyYOsD69wN+GxEbAhcDq0XE54HjgeMjYvwQfEZflgK+A2wNLC9pifL8PRGxCfBPYFtgO2Cu8txfgfcP8nPMzMzMzMxm4RFZZmZmZmZmQ+O4iPhJr+cejIhLao/fBmxQ7i/1BuBNwFvIEU8A9wyw/lXIBizIkVn9mZ3P6MtUYB9gT2BRYGx5/q7y398AK5LXl+8uo9AWAB4e5OeYmZmZmZnNwiOyzMzMzMzMhs/LvR7/Hji5TBd4FDly6S/AquX1tQZY1yPAeuXvL5CNS5DTGM4HIEmz+Rl92Rv4ObAz8O/a8+uU/64BPF4+94LyuYcADw3yc8zMzMzMzGbhhiwzMzMzM7PW+T6wdZmCcH/gifLch8pIpoUGeO+ZwNplubWBc8vz1wM7SLod2Gg2P6Mv1wOfB24qj99U/ruRpJuBJYDLgSuApctzx5KNZ2ZmZmZmZrNFETHS22BmZmZmZmZmZmZmZmY2C4/IMjMzMzMzMzMzMzMzs7bkhiwzMzMzMzMzMzMzMzNrS27IMjMzMzMzMzMzMzMzs7bkhiwzMzMzMzMzMzMzMzNrS27IMjMzMzMzMzMzMzMzs7bkhiwzMzMzMzMzMzMzMzNrS27IMjMzMzMzMzMzMzMzs7b0/6GqhLIw2+pXAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(df_tokens[\"labels\"], df_tokens[\"predicted_label\"],\n",
    "                      tags.names)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "         0     1     2      3      4      5      6      7      8      9    \\\ntokens     其     中     ，      招      商      +      城      建      与      中   \nlabels     O     O     O      O      O      O      O      O      O      O   \npreds      O     O     O  B-受让人  I-受让人  I-受让人  I-受让人  I-受让人  I-受让人  I-受让人   \nlosses  0.00  0.00  0.00   2.38   3.81   4.25   3.74   4.02   3.98   4.47   \n\n        ...   116   117   118   119   120   121   122   123   124    125  \ntokens  ...     轮     竞     价     无     房     企     退     出     。  [SEP]  \nlabels  ...     O     O     O     O     O     O     O     O     O    IGN  \npreds   ...     O     O     O     O     O     O     O     O     O      O  \nlosses  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00   0.00  \n\n[4 rows x 126 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>116</th>\n      <th>117</th>\n      <th>118</th>\n      <th>119</th>\n      <th>120</th>\n      <th>121</th>\n      <th>122</th>\n      <th>123</th>\n      <th>124</th>\n      <th>125</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>tokens</th>\n      <td>其</td>\n      <td>中</td>\n      <td>，</td>\n      <td>招</td>\n      <td>商</td>\n      <td>+</td>\n      <td>城</td>\n      <td>建</td>\n      <td>与</td>\n      <td>中</td>\n      <td>...</td>\n      <td>轮</td>\n      <td>竞</td>\n      <td>价</td>\n      <td>无</td>\n      <td>房</td>\n      <td>企</td>\n      <td>退</td>\n      <td>出</td>\n      <td>。</td>\n      <td>[SEP]</td>\n    </tr>\n    <tr>\n      <th>labels</th>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>...</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>IGN</td>\n    </tr>\n    <tr>\n      <th>preds</th>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>B-受让人</td>\n      <td>I-受让人</td>\n      <td>I-受让人</td>\n      <td>I-受让人</td>\n      <td>I-受让人</td>\n      <td>I-受让人</td>\n      <td>I-受让人</td>\n      <td>...</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n    </tr>\n    <tr>\n      <th>losses</th>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>2.38</td>\n      <td>3.81</td>\n      <td>4.25</td>\n      <td>3.74</td>\n      <td>4.02</td>\n      <td>3.98</td>\n      <td>4.47</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 126 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "         0     1     2     3     4     5     6     7     8     9    ...   316  \\\ntokens     近     日     ，     长     兴     县     自     然     资     源  ...     长   \nlabels     O     O     O  B-城市  I-城市  I-城市     O     O     O     O  ...     O   \npreds      O     O     O     O     O     O     O     O     O     O  ...     O   \nlosses  0.06  0.04  0.00  1.96  2.54  3.60  0.01  0.01  0.01  0.01  ...  0.04   \n\n         317   318   319   320   321   322   323   324    325  \ntokens     兴     县     人     民     政     府     综     合  [SEP]  \nlabels     O     O     O     O     O     O     O     O    IGN  \npreds      O     O     O     O     O     O     O     O      O  \nlosses  0.02  0.02  0.00  0.00  0.00  0.00  0.00  0.00   0.00  \n\n[4 rows x 326 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>316</th>\n      <th>317</th>\n      <th>318</th>\n      <th>319</th>\n      <th>320</th>\n      <th>321</th>\n      <th>322</th>\n      <th>323</th>\n      <th>324</th>\n      <th>325</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>tokens</th>\n      <td>近</td>\n      <td>日</td>\n      <td>，</td>\n      <td>长</td>\n      <td>兴</td>\n      <td>县</td>\n      <td>自</td>\n      <td>然</td>\n      <td>资</td>\n      <td>源</td>\n      <td>...</td>\n      <td>长</td>\n      <td>兴</td>\n      <td>县</td>\n      <td>人</td>\n      <td>民</td>\n      <td>政</td>\n      <td>府</td>\n      <td>综</td>\n      <td>合</td>\n      <td>[SEP]</td>\n    </tr>\n    <tr>\n      <th>labels</th>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>B-城市</td>\n      <td>I-城市</td>\n      <td>I-城市</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>...</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>IGN</td>\n    </tr>\n    <tr>\n      <th>preds</th>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>...</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n    </tr>\n    <tr>\n      <th>losses</th>\n      <td>0.06</td>\n      <td>0.04</td>\n      <td>0.00</td>\n      <td>1.96</td>\n      <td>2.54</td>\n      <td>3.60</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>...</td>\n      <td>0.04</td>\n      <td>0.02</td>\n      <td>0.02</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 326 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "           0       1       2       3       4       5     6     7     8    \\\ntokens       1       -       4       号       地       块     ，     总     建   \nlabels  B-地块编码  I-地块编码  I-地块编码  I-地块编码  I-地块编码  I-地块编码     O     O     O   \npreds        O  I-地块编码  I-地块编码  I-地块编码  I-地块编码  I-地块编码     O     O     O   \nlosses    1.23    0.30    0.25    0.13    0.17    0.38  0.00  0.00  0.00   \n\n         9    ...   314   315   316   317   318   319   320   321   322    323  \ntokens     面  ...     价     3     8     1     7     元     /     ㎡     。  [SEP]  \nlabels     O  ...     O     O     O     O     O     O     O     O     O    IGN  \npreds      O  ...     O     O     O     O     O     O     O     O     O      O  \nlosses  0.00  ...  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00   0.00  \n\n[4 rows x 324 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>314</th>\n      <th>315</th>\n      <th>316</th>\n      <th>317</th>\n      <th>318</th>\n      <th>319</th>\n      <th>320</th>\n      <th>321</th>\n      <th>322</th>\n      <th>323</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>tokens</th>\n      <td>1</td>\n      <td>-</td>\n      <td>4</td>\n      <td>号</td>\n      <td>地</td>\n      <td>块</td>\n      <td>，</td>\n      <td>总</td>\n      <td>建</td>\n      <td>面</td>\n      <td>...</td>\n      <td>价</td>\n      <td>3</td>\n      <td>8</td>\n      <td>1</td>\n      <td>7</td>\n      <td>元</td>\n      <td>/</td>\n      <td>㎡</td>\n      <td>。</td>\n      <td>[SEP]</td>\n    </tr>\n    <tr>\n      <th>labels</th>\n      <td>B-地块编码</td>\n      <td>I-地块编码</td>\n      <td>I-地块编码</td>\n      <td>I-地块编码</td>\n      <td>I-地块编码</td>\n      <td>I-地块编码</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>...</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>IGN</td>\n    </tr>\n    <tr>\n      <th>preds</th>\n      <td>O</td>\n      <td>I-地块编码</td>\n      <td>I-地块编码</td>\n      <td>I-地块编码</td>\n      <td>I-地块编码</td>\n      <td>I-地块编码</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>...</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n      <td>O</td>\n    </tr>\n    <tr>\n      <th>losses</th>\n      <td>1.23</td>\n      <td>0.30</td>\n      <td>0.25</td>\n      <td>0.13</td>\n      <td>0.17</td>\n      <td>0.38</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 324 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_samples(df):\n",
    "    for _, row in df.iterrows():\n",
    "        labels, preds, tokens, losses = [], [], [], []\n",
    "        for i, mask in enumerate(row[\"attention_mask\"]):\n",
    "            if i not in {0, len(row[\"attention_mask\"])}:\n",
    "                labels.append(row[\"labels\"][i])\n",
    "                preds.append(row[\"predicted_label\"][i])\n",
    "                tokens.append(row[\"input_tokens\"][i])\n",
    "                losses.append(f\"{row['loss'][i]:.2f}\")\n",
    "        df_tmp = pd.DataFrame({\"tokens\": tokens, \"labels\": labels,\n",
    "                               \"preds\": preds, \"losses\": losses}).T\n",
    "        yield df_tmp\n",
    "\n",
    "df[\"total_loss\"] = df[\"loss\"].apply(sum)\n",
    "df_tmp = df.sort_values(by=\"total_loss\", ascending=False).head(3)\n",
    "\n",
    "for sample in get_samples(df_tmp):\n",
    "    display(sample)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "df_tmp = df.loc[df[\"labels\"].apply(lambda x: \"成交\" in x)].head(2)\n",
    "for sample in get_samples(df_tmp):\n",
    "    display(sample)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "df.to_csv(\"result.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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